scholarly journals BrainFreeze: Expanding the Capabilities of Neuromorphic Systems Using Mixed-Signal Superconducting Electronics

2021 ◽  
Vol 15 ◽  
Author(s):  
Paul Tschirhart ◽  
Ken Segall
Keyword(s):  
Computational Neuroscience ◽  
Large Scale ◽  
Learning Support ◽  
Neuron Models ◽  
Neuromorphic Systems ◽  
On Line ◽  
Superconducting Electronics ◽  
Near Term ◽  
On Line Learning ◽  
New Generation

Superconducting electronics (SCE) is uniquely suited to implement neuromorphic systems. As a result, SCE has the potential to enable a new generation of neuromorphic architectures that can simultaneously provide scalability, programmability, biological fidelity, on-line learning support, efficiency and speed. Supporting all of these capabilities simultaneously has thus far proven to be difficult using existing semiconductor technologies. However, as the fields of computational neuroscience and artificial intelligence (AI) continue to advance, the need for architectures that can provide combinations of these capabilities will grow. In this paper, we will explain how superconducting electronics could be used to address this need by combining analog and digital SCE circuits to build large scale neuromorphic systems. In particular, we will show through detailed analysis that the available SCE technology is suitable for near term neuromorphic demonstrations. Furthermore, this analysis will establish that neuromorphic architectures built using SCE will have the potential to be significantly faster and more efficient than current approaches, all while supporting capabilities such as biologically suggestive neuron models and on-line learning. In the future, SCE-based neuromorphic systems could serve as experimental platforms supporting investigations that are not feasible with current approaches. Ultimately, these systems and the experiments that they support would enable the advancement of neuroscience and the development of more sophisticated AI.

Nonlinear Control of Dynamic Systems Using Single Multiplicative Neuron Models

2012 ◽  
Author(s):  
Jonathan G. Turner ◽  
Biswanath Samanta
Keyword(s):  
Nonlinear Control ◽  
Dynamic Systems ◽  
Optimization Technique ◽  
Real Life ◽  
Population Based ◽  
Motor Speed ◽  
Neuron Models ◽  
On Line ◽  
On Line Learning ◽  
Better Than

The paper presents an approach to nonlinear control of dynamic systems using artificial neural networks (ANN). A novel form of ANN, namely, single multiplicative neuron (SMN) model is proposed in place of more traditional multi-layer perceptron (MLP). SMN derives its inspiration from the single neuron computation model in neuroscience. SMN model is trained off-line, to estimate the network weights and biases, using a population based stochastic optimization technique, namely, particle swarm optimization (PSO). Both off-line training and on-line learning of SMN have been considered. The development of the control algorithm is illustrated through the hardware-in-the-loop (HIL) implementation of DC motor speed control in LabVIEW environment. The controller based on SMN performs better than MLP. The simple structure and faster computation of SMN have the potential to make it a preferred candidate for implementation of real-life complex control systems.

scholarly journals Secure multivariate large-scale multi-centric analysis through on-line learning: an imaging genetics case study

2017 ◽  
Author(s):  
Marco Lorenzi ◽  
Boris Gutman ◽  
Paul M. Thompson ◽  
Daniel C. Alexander ◽  
Sebastien Ourselin ◽  
...  
Keyword(s):  
Large Scale ◽  
Imaging Genetics ◽  
On Line ◽  
On Line Learning

scholarly journals Analysis of Online Learning Behavior of College Students during the Anti-epidemic Period—Take Suqian College as an Example

2021 ◽  
Vol 5 (2) ◽  
pp. p46
Author(s):  
Yang Ling ◽  
Zhang Lei
Keyword(s):  
College Students ◽  
Online Learning ◽  
Large Scale ◽  
Ministry Of Education ◽  
Learning Behavior ◽  
Learning Efficiency ◽  
Epidemic Period ◽  
Teachers And Students ◽  
On Line ◽  
On Line Learning

Affected by the COVID-19 epidemic, in order to prevent the virus from spreading again caused by students’ off-line learning and realize the normalization of anti-epidemic, colleges and universities have implemented the relevant requirements of the Ministry of Education on “suspension of classes and non-stop learning” during the epidemic, and college students across the country have started large-scale on-line learning. Facing this reality, teachers and students actively cooperate with online learning. However, with the development of online learning, college students are gradually exposed to low learning efficiency. Taking Suqian University as an example, this paper conducts an empirical study to analyze college students’ online learning behavior and its influencing factors, and puts forward an improvement path to optimize college students’ online learning behavior and improve online learning efficiency.

scholarly journals Multi-Terminal Memristive Devices Enabling Tunable Synaptic Plasticity in Neuromorphic Hardware: A Mini-Review

2021 ◽  
Vol 3 ◽  
Author(s):  
Yann Beilliard ◽  
Fabien Alibart
Keyword(s):  
Synaptic Plasticity ◽  
Computational Neuroscience ◽  
Complementary Metal Oxide Semiconductor ◽  
Oxide Semiconductor ◽  
Network Activity ◽  
Neuromorphic Hardware ◽  
Learning Capabilities ◽  
On Line ◽  
Heterosynaptic Plasticity ◽  
On Line Learning

Neuromorphic computing based on spiking neural networks has the potential to significantly improve on-line learning capabilities and energy efficiency of artificial intelligence, specially for edge computing. Recent progress in computational neuroscience have demonstrated the importance of heterosynaptic plasticity for network activity regulation and memorization. Implementing heterosynaptic plasticity in hardware is thus highly desirable, but important materials and engineering challenges remain, calling for breakthroughs in neuromorphic devices. In this mini-review, we propose an overview of the latest advances in multi-terminal memristive devices on silicon with tunable synaptic plasticity, enabling heterosynaptic plasticity in hardware. The scalability and compatibility of the devices with industrial complementary metal oxide semiconductor (CMOS) technologies are discussed.

Patterns of Implicit Learning Below the Level of Conscious Knowledge

2010 ◽  
Vol 24 (2) ◽  
pp. 91-101 ◽  
Author(s):  
Juliana Yordanova ◽  
Rolf Verleger ◽  
Ullrich Wagner ◽  
Vasil Kolev
Keyword(s):  
Implicit Learning ◽  
Explicit Knowledge ◽  
Implicit Knowledge ◽  
Knowledge Generation ◽  
Sleep Stages ◽  
Implicit Processing ◽  
Implicit Processes ◽  
Late Night ◽  
On Line ◽  
On Line Learning

The objective of the present study was to evaluate patterns of implicit processing in a task where the acquisition of explicit and implicit knowledge occurs simultaneously. The number reduction task (NRT) was used as having two levels of organization, overt and covert, where the covert level of processing is associated with implicit associative and implicit procedural learning. One aim was to compare these two types of implicit processes in the NRT when sleep was or was not introduced between initial formation of task representations and subsequent NRT processing. To assess the effects of different sleep stages, two sleep groups (early- and late-night groups) were used where initial training of the task was separated from subsequent retest by 3 h full of predominantly slow wave sleep (SWS) or rapid eye movement (REM) sleep. In two no-sleep groups, no interval was introduced between initial and subsequent NRT performance. A second aim was to evaluate the interaction between procedural and associative implicit learning in the NRT. Implicit associative learning was measured by the difference between the speed of responses that could or could not be predicted by the covert abstract regularity of the task. Implicit procedural on-line learning was measured by the practice-based increased speed of performance with time on task. Major results indicated that late-night sleep produced a substantial facilitation of implicit associations without modifying individual ability for explicit knowledge generation or for procedural on-line learning. This was evidenced by the higher rate of subjects who gained implicit knowledge of abstract task structure in the late-night group relative to the early-night and no-sleep groups. Independently of sleep, gain of implicit associative knowledge was accompanied by a relative slowing of responses to unpredictable items suggesting reciprocal interactions between associative and motor procedural processes within the implicit system. These observations provide evidence for the separability and interactions of different patterns of processing within implicit memory.

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